Industrial internet of things with sensor network platform in front sub platform type and control method thereof

ABSTRACT

The present disclosure discloses an industrial Internet of things with a sensor network platform in a front sub platform type, comprising: a user platform, a service platform, a management platform, a sensor network platform, and an object platform which are interacted sequentially. The sensor network platform adopts front sub platform layout, which can process data for different target objects, and then summarize data, so as to reduce the data processing capacity of entire sensor network platform. The management platform is arranged independently to form multiple independent data processing channels of the same or different. The gateway on each communication network shares part of the calculation for the management platform, effectively reducing the calculation pressure of the management platform, and ensuring that the data is transmitted according to a specific path or processed by a specific server, so as to ensure the safety and independence of the data.

TECHNICAL FIELD

The present disclosure generally relates to intelligent manufacturingtechnology, in particular, to industrial Internet of things with asensor network platform in a front sub platform type and control methodthereof.

BACKGROUND

In industry, the intelligent manufacturing of assembly line products byintelligent production is developing rapidly. The intelligentmanufacturing of assembly line products involves multiple processes,device and auxiliary instruments. In order to facilitate intelligentcontrol, it is often necessary to manage or control the use parameters,product manufacturing parameters and other data of some processes,device and auxiliary instruments. However, due to the large amount ofsuch data, and the increased processes of the assembly line, theincrease of the number of devices in different processes and otherfactors, the requirements for data processing, classification andtransmission of a data control interaction center are too high, resultin high computing pressure and high cost for the data controlinteraction center. At present, in the intelligent manufacturingprocess, it is not possible to achieve global intelligent control overthe entire assembly line.

SUMMARY

The technical problem to be solved in the present disclosure is toprovide an industrial Internet of things with a sensor network platformin a front sub platform type, which performs data classification,processing, coordination and unification by fitting process sequences ofproduct manufacturing, makes full use of the information processing,transmission and storage capacity of different architectures by usingdifferent architectures of the Internet of things, and standardizes andclassifies the corresponding information according to unified standards,reducing the level of information processing demand.

The present disclosure is realized by the following technical scheme:Industrial Internet of things with a sensor network platform in a frontsub platform type, comprising: a user platform, a service platform, amanagement platform, a sensor network platform, and an object platformwhich are interacted sequentially, wherein the user platform isconfigured as a terminal device interacting with a user; the serviceplatform is configured as a first server and configured to: receive aninstruction of the user platform and send them to the managementplatform, and extract information required for the user platform fromthe management platform, processing the information, and send theinformation to the user platform; the management platform is configuredas a second server and configured to control an operation of the objectplatform, and receive feedback data of the object platform; the sensornetwork platform is configured as a communication network and a gatewayand configured to for interaction between the object platform and themanagement platform; the object platform is configured as productionline devices and device data collectors for manufacture.

The service platform adopts centralized layout, the management platformadopts independent layout, and the sensor network platform adopts frontsub platform layout; wherein the centralized layout means that theservice platform uniformly receives data, uniformly processes data anduniformly sends data; the independent layout means that the managementplatform adopts different sub platforms for data storage, dataprocessing and/or data transmission for different types of data;

the front sub platform layout means that the sensor network platform isprovided with a general platform and a plurality of sub platforms, theplurality of sub platforms respectively store and process data ofdifferent types or data of different receiving objects sent by theobject platform, and the general platform stores and processes data ofthe plurality of sub platforms after summarizing the data of theplurality of sub platforms, and transmits the data to the managementplatform; the production line devices and the device data collectors ofthe object platform are divided into a plurality of target objects indifferent process sequences according to processes of manufacturing anassembly line product, and a device data collector of a same targetobject is used to collect real-time data of production line device of acorresponding process; each sub platform of the sensor network platformcorresponds to target object data of each of different processsequences, the target object data includes threshold data of aproduction line device stored in each sub platform database andreal-time data collected by a device data collector, the sub platformdatabase is configured in the gateway; when the real-time data of theproduction line device is greater than the threshold data, the subplatform corresponding to the production line device retrieves thetarget object data in the corresponding sub platform database, packagesthe data and transmits it to the general platform of the sensor networkplatform; the general platform of the sensor network platform receivesthe packaged data, generates compiled files sorted according to theprocess sequences based on the corresponding production line device andthe device data collector, classifies the compiled files according tothe process sequences, and stores them in the second server; the firstserver receives or retrieves the compiled files in correspondingsequences, analyzes the compiled files, and sends different controlinstructions to the corresponding second server based on analysisresults; the first server sorts and classifies the control instructionsaccording to the process sequences of the object platform, and generatesclassified control instructions corresponding to the process sequences,the second server generates configuration files of different typescorresponding to the process sequences according to the classifiedcontrol instructions, and sends the configuration files to the generalplatform of the sensor network platform for summary and storage; eachsub platform database of the sensor network platform corresponds to eachinformation channel, the general platform of the sensor network platformsends the configuration files to the corresponding sub platforms,respectively, and the sub platforms control the corresponding productionline devices and device data collectors to execute the correspondingcontrol instructions according to the configuration files, respectively.

Based on the above IOT technical scheme, when there are one or more subprocesses in a same process, the production line devices and device datacollectors corresponding to the one or more sub processes are dividedinto multiple sub target objects with different sub process sequencesaccording to a sequence of manufacturing the assembly line product, subtarget object data of each sub target object includes threshold data andreal-time data corresponding to each sub target object, all sub targetobject data are sorted according to the sequence of manufacturing theassembly line product and then packaged and summarized, and used astarget object data of the same process.

Based on the above IOT technical scheme, the threshold data of theproduction line device includes a fixed parameter value of a maximumthreshold allowed by the corresponding production line device duringproduct manufacturing, the real-time data is a real-time parameter valuecollected by the device data collector of the corresponding productionline device according to predetermined time, and the real-time parametervalue and the fixed parameter value belong to a same parameter type.

Based on the above IOT technical scheme, the threshold data of theproduction line device also includes an early warning parameter valuecorresponding to an early warning threshold set by the production linedevice during the product manufacturing, and the early warning parametervalue is 70%˜90% of the fixed parameter value.

Based on the above IOT technical scheme, when the real-time parametervalue of each of the device data collector is greater than the earlywarning parameter value, the sub platform corresponding to theproduction line device also retrieves the target object data in thecorresponding sub platform database and packages the data to the generalplatform of the sensor network platform; at this time, the target objectdata includes the early warning parameter value and the real-timeparameter value.

Based on the above IOT technical scheme, when the real-time parametervalue is greater than the early warning parameter value and the fixedparameter value, the sub platform corresponding to the production linedevice takes the corresponding fixed parameter value as priority data,and takes the fixed parameter value and the real-time parameter value astarget object data in priority to package them to the general platformof the sensor network platform.

Based on the above IOT technical scheme, the general platform of thesensor network platform receives the packaged data, generates compiledfiles sorted by the process sequences according to the corresponding theproduction line device and the device data collectors, classifies thecompiled files according to the process sequences, and stores them inthe second server includes that: the general platform of the sensornetwork platform stores threshold data tables and real-time data tablessorted according to the process sequences in advance; the generalplatform of the sensor network platform receives the packaged data andcompiles it according to the process sequences, and compiles thethreshold data and the real-time data of all the same processes into thecorresponding threshold data tables and real-time data tables to formsequential compiled files; the second server is arranged with aplurality of independent sub platform servers according to the processsequences, and the compiled files are stored through independent subplatform servers of the corresponding processes, respectively.

Based on the above IOT technical scheme, the first server receives orretrieves the compiled files in corresponding sequences and analyzes thecompiled files, and sends different control instructions to thecorresponding second server based on analysis results further includesthat: the first server is preset with a real area map matching withinstallation environment of the production line devices, the productionline devices in the real area map are named successively according tothe process sequences of manufacturing the assembly line product, andeach production line device has a parameter comparison table; theparameter comparison table at least comprises a standard data column anda comparison data column; after the first server receives or retrievesthe compiled files in the corresponding sequences, the compiled filesare divided into sections successively according to the processsequences, and the target object data in the compiled filescorresponding to each section is mapped to the parameter comparisontable of the production line device of the corresponding process,wherein the threshold data and the real-time data correspond to thestandard data column and the comparison data column, respectively; whenthe real-time data exceeds the threshold data, the first serverassociates and identifies the corresponding production line device inthe real area map; the first server sends the different controlinstructions to different identified production line device, packagesall the different control instructions successively according to theprocess sequences, and then transmits them to the corresponding secondserver. Based on the above IOT technical scheme, each production linedevice in the real area map is also configured with a controlinstruction table, the control instruction table stores a plurality ofthe control instructions and control instruction data packets associatedwith the control instructions, when the first server sends controlinstructions corresponding to different control instruction tables, thecontrol instruction data packets of different control instructions arepackaged successively according to the process sequences and transmittedto the corresponding second server.

Based on the above IOT technical scheme, in the compiled files, datasegmentation characters are set between different processes, and thecompiled files are segmented into node data through the datasegmentation characters, the number of the node data is the same as thenumber of processes of manufacturing the assembly line product.

Compared with the prior art, the beneficial effects of the invention areas follows: the disclosure uses a five-platform structure to build theInternet of things, wherein the sensor network platform is arranged in afront sub platform, which can process data for different target objects,and then summarize data. A large amount of data can be processed firstand then summarized, so as to reduce data processing capacity of theentire sensor network platform and avoid heavy load operation caused bydata clutter. The management platform is arranged independently, formingmultiple independent data processing channels of the same or different.Each data processing channel undertakes part of the calculation of themanagement platform, effectively reducing the computing pressure of themanagement platform, ensuring that the data is transmitted according toa specific path or processed by a specific server, and ensuring thesafety and independence of the data. Finally, the service platform isarranged in a centralized manner to facilitate the collection of alldata or the coordinated and unified processing of all target objects, sothat the service platform can better control the Internet of things.

BRIEF DESCRIPTION OF THE DRAWINGS

This specification will be further described in the form of exemplaryembodiments, which will be described in detail by the accompanyingdrawings. These embodiments are not restrictive. In these embodiments,the same number represents the same structure, wherein:

FIG. 1 shows a structural framework of the industrial Internet of thingswith a sensor network platform in a front sub platform type according tosome embodiments in the present disclosure;

FIG. 2 shows a flow chart of the control method of the industrialInternet of things with a sensor network platform in a front subplatform type according to some embodiments in the present disclosure;

FIG. 3 shows an exemplary flowchart for determining a broadbandallocation scheme according to some embodiments in the presentdisclosure;

FIG. 4 shows an exemplary flowchart for preprocessing monitoring dataaccording to some embodiments in the present disclosure;

FIG. 5 shows an exemplary flowchart for determining a sampling rate ofthe monitoring data according to some embodiments in the presentdisclosure.

DETAILED DESCRIPTION

In order to more clearly explain the technical scheme of the embodimentsof the present disclosure, the following will briefly introduce thedrawings that need to be used in the description of the embodiments.Obviously, the drawings in the following description are only someexamples or embodiments of the present disclosure. For those skilled inthe art, the present disclosure can also be applied to other similarscenarios according to these drawings without creative work. Unless itis obvious from the language environment or otherwise stated, the samelabel in the figure represents the same structure or operation.

It should be understood that the “system”, “device”, “unit” and/or“module” used herein is a method for distinguishing differentcomponents, elements, components, parts or assemblies at differentlevels. However, if other words serve the same purpose, they may bereplaced by other expressions.

As used herein, the singular forms “a,” “an,” and “the” may be intendedto include the plural forms as well, unless the context clearlyindicates otherwise; and the plural forms may be intended to include thesingular forms as well, unless the context clearly indicates otherwise.Generally speaking, the terms “include” and “comprise” only indicatethat the steps and elements that have been clearly identified areincluded, and these steps and elements do not constitute an exclusivelist. Methods or device may also contain other steps or elements.

A flowchart is used in the present disclosure to explain the operationperformed by the system according to the embodiment of the presentdisclosure. It should be understood that the preceding or subsequentoperations are not necessarily performed accurately in sequence.Instead, you can process the steps in reverse order or simultaneously.At the same time, you can add other operations to these procedures, orremove one or more operations from these procedures.

FIG. 1 shows a structural framework of the industrial Internet of things(IOT) with a sensor network platform in a front sub platform typeaccording to some embodiments in the present disclosure.

As shown in FIG. 1 , the Industrial IOT with a sensor network platformin a front sub platform type comprises: a user platform, a serviceplatform, a management platform, a sensor network platform and an objectplatform which are interacted sequentially.

The user platform is configured as a terminal device interacting with auser. The service platform is configured as a first server andconfigured to: receive instructions of the user platform and send themto the management platform, and extract information required forprocessing the user platform from the management platform and send theinformation to the user platform. The management platform is configuredas a second server, and configured to control an operation of the objectplatform, and receive feedback data of the object platform. The sensornetwork platform is configured as a communication network and a gatewayand configured to perform interaction between the object platform andthe management platform. The object platform is configured as productionline devices and device data collectors for perform manufacture.

The service platform may adopt centralized layout, the managementplatform may adopt independent layout, and the sensor network platformmay adopt front sub platform layout. The centralized layout may meanthat the service platform uniformly receives data, uniformly processesdata and uniformly sends data. The independent layout may mean that themanagement platform adopts different sub platforms for data storage,data processing and/or data transmission for different types of data.The front sub platform layout may mean that the sensor network platformis provided with a general platform and a plurality of sub platforms,the plurality of sub platforms respectively store and process data ofdifferent types or data of different receiving objects sent by theobject platform, and the general platform stores and processes data ofthe plurality of sub platforms after summarizing the data of theplurality of sub platforms, and transmits the data to the managementplatform. The production line devices and the device data collectors ofthe object platform may be divided into a plurality of target objects indifferent process sequences according to processes of manufacturing anassembly line product, and a device data collector of a same targetobject may be used to collect real-time data of the production linedevice of a corresponding process. Each sub platform of the sensornetwork platform may respectively correspond to target object data ofeach of different process sequences, the target object data may includethreshold data of the production line device stored in each sub platformdatabase and the real-time data collected by the device data collector,the sub platform data database may be configured in the gateway. Whenthe real-time data of the production line device is greater than thethreshold data, the sub platform corresponding to the production linedevice may call the target object data in the corresponding sub platformdatabase, package the data and transmit it to the general platform ofthe sensor network platform. The general platform of the sensor networkplatform may receive the packaged data, generate compiled files sortedby the process sequences according to the corresponding the productionline devices and the device data collectors, classify the compiled filesaccording to the process sequences and store them in the second server.The first server may receive or retrieve the compiled files incorresponding sequences and analyze the compiled files, and senddifferent control instructions to the corresponding second server basedon analysis results. The first server may sort and classify the controlinstructions according to the process sequences of the object platform,and generate classified control instructions corresponding to theprocess sequences, the second server may generate configuration files ofdifferent types corresponding to the process sequences according to theclassified control instructions, and send the configuration files to thegeneral platform of the sensor network platform for summary and storage.Each sub platform database of the sensor network platform may correspondto each information channel, the general platform of the sensor networkplatform may send the configuration files to the corresponding subplatforms, respectively, and the sub platforms may control thecorresponding production line devices and device data collectors toexecute the corresponding control instructions according to theconfiguration files, respectively.

In the existing technology, in the field of intelligent manufacturingtechnology, the products may be manufactured according to a process ofan assembly line. As some electronic products and mechanical productsinvolve the assembly of parts and components, they not only involvecomplex processes and devices, but also involve complex intelligentcontrol. These may form huge, complex and different types of datainformation. In the existing technology, the complex operations in anassembly line are basically classified or managed by sections accordingto workshops, processes, device, etc., and it is impossible tocompletely and uniformly control the entire operations in an assemblyline. To do this, not only expensive cost investment, large amount ofinformation transmission and processing, but also high requirements fordevice intelligence are required. Moreover, different control parametersor working parameters brought by complex processes and various deviceare more cumbersome, and various data may have different communicationmodes, which is very difficult to implement.

In this embodiment, a five-platform structure is used to build theInternet of things. The sensor network platform adopts the front subplatform layout, which can process data for different target objects,and then summarize data. A large amount of data can be processed firstand then summarized, so as to reduce the data processing capacity of theentire sensor network platform and avoid heavy load operation caused bydata clutter. The management platform is arranged independently to formmultiple independent data processing channels of the same or different.Each data processing channel undertakes part of the calculation of themanagement platform, effectively reducing the calculation pressure ofthe management platform, and ensuring that the data is transmittedaccording to a specific path or processed by a specific server, so as toensure the safety and independence of the data. Finally, the serviceplatform adopts centralized layout to facilitate the collection of alldata or the coordinated and unified processing of all target objects, sothat the service platform and/or the user platform can better controlthe Internet of things.

It should be noted that the user platform in this embodiment may be adesktop computer, tablet computer, notebook computer, mobile phone orother electronic devices that can realize data processing and datacommunication, and is not limited here. In specific applications, thefirst server and the second server may adopt a single server or a servercluster, and there are no too many restrictions here. It should beunderstood that the data processing process mentioned in this embodimentmay be processed by the processor of the server, and the data stored inthe server may be stored on the storage device of the server, such asthe hard disk and other memories. In specific applications, the sensornetwork platform may adopt multiple groups of gateway servers ormultiple groups of intelligent routers, which are not limited here. Itshould be understood that the data processing process mentioned in theembodiment may be processed by the processor of the gateway server, andthe data stored in the gateway server may be stored on the storagedevice of the gateway server, such as hard disk, SSD and other memories.

Further description, in this embodiment, the sensor network platformadopts the front sub platform layout, that is, it receives and processesthe relevant data of the production line device and device datacollector of the same process through multiple sub platforms, and thenuses the general platform to process and summarize all the dataaccording to the process sequences, so as to sort all the data accordingto the product process, which not only achieves orderly data processing,but also ensure that the data of different processes are independent andunaffected. Each sub platform and the general platform can share thedata processing capacity with each other, thus reducing the computingpressure of the Internet of things. At the same time, when interactingwith the management platform, data transmission and processing may beperformed independently through multiple independent second serversaccording to the process, so as to achieve classification management andmonitoring, and the data source is clear, which is also convenient fordata processing, classification and management.

In some embodiments, the production line device may include/be all kindsof production line devices supported by the assembly line for productmanufacturing. Taking mechanical products as an example, the productionline device may be a component assembly device, an assembly device, adetection device, etc. Furthermore, taking an automobile engine assemblyline as an example, the production line device may be cylinderprocessing device, cylinder positioning and turnover device, camassembly installation device, bolt assembly installation equipment,machine filter assembly, oiling device, etc. Accordingly, the thresholddata of the production line device may be a fixed parameter valuecorresponding to the maximum threshold value allowed by the productionline device during product manufacturing, such as the maximumtemperature value, air pressure value, oil pressure value, load amount,production amount per unit time, vibration amount, etc. When parametersof the production line device exceed the threshold value, it mayindicate that the device is in abnormal or even in dangerous workingstate, thus, corresponding operations may be made through monitoring thedata.

In some embodiments, the real-time data may be the real-time parametervalue collected by the device data collector corresponding to theproduction line device according to the given time. The device datacollectors may be divided into many types according to the differentreal-time data collected, such as a temperature sensor for collectingtemperature, a pressure sensor for collecting pressure, a weight sensorfor collecting weight, a counter for calculating the product quantity, avibration sensor for collecting vibration frequency, etc.Correspondingly, the real-time parameter value is the real-time data ofthe production line device collected by the corresponding device datacollector, for example, it may actually be/include temperature value,pressure value, product load amount value, production amount per unittime, device vibration frequency, etc. In specific application, thereal-time parameter value and the fixed parameter value belong to thesame parameter type, convenient for comparing the parameters of acertain production line device, so as to monitor the device workingconditions, protect the device and product manufacturing, or collectmore real-time parameters associated with the production line device, soas to have a more comprehensive understanding of the production linedevice working conditions.

When the real-time data of the production line device is greater thanthe threshold data, it means that the real-time parameters correspondingto the production line device may exceed the set working state of theproduction line device, which may lead to a production line devicefailure, dangerous operation, overload operation, etc. At this time, thesensor network platform may compare the real-time data with thethreshold data, and the general platform may generate the compiled filessorted according to the process sequences corresponding to the real-timedata and upload it, thus, the first server may issue controlinstructions to perform corresponding operations on the production linedevice and/or device data collector (such as controlling the productionline device to reduce the temperature and pressure, reducing the workingspeed, reducing the product feed, and sending out alarm alarms, etc.),so as to carry out intelligent device control and reduce productionrisks.

In specific applications, there may be several sub processes formedthrough several production line device in one process, all sub processestogether form a large process. For example, in an automobiledistribution assembly line, when installing a cam, there may be eightsub processes in total, which specifically include loosening the bearingcap, removing the bearing cap, installing the upper and lower bearingpads, installing the piston cooling nozzle, inserting the camshaftdriving key, installing the camshaft thrust plate, lifting thecrankshaft, and driving the key. Based on this, when there are severalsub processes in the same process, the production line device and devicedata collectors corresponding to several sub processes may be dividedinto multiple sub target objects with different sub process sequencesaccording to sequences of manufacturing an assembly line product. Thethreshold data and real-time data corresponding to each sub targetobject may be sub target object data of the corresponding sub targetobject, all sub target object data may be sorted according to theproduct manufacturing sequences and then packaged and summarized astarget object data of the operation.

In the specific application, the threshold data of the production linedevice may also include an early warning parameter value correspondingto a warning threshold set by the production line device during productmanufacturing, and the warning parameter value may be 70%˜90% of thefixed parameter value. When the real-time parameter value of the devicedata collector is greater than the early warning parameter value, thesub platform corresponding to the production line device may alsoretrieve the target object data in the corresponding sub platformdatabase and package the data to the general platform of the sensornetwork platform. At this time, the target object data may include theearly warning parameter value and the real-time parameter value. Theremay be data delay during the collection and transmission of thereal-time data of the production line device. During this period whensuch data is finally feed back to the management platform and theservice platform, corresponding operations may be carried out throughcontrol instructions feedback by the service platform, a process timemay be required. If any platform is delayed or the feedback is too slow,a best processing time may be missed, resulting in correspondingaccidents of the production line device. Therefore, the presentdisclosure also sets the corresponding early-warning parameter value onthe production line device or the sub platform of the production linedevice, which is less than the fixed parameter value. Therefore, whenthe real-time parameter value is greater than the early-warningparameter value, the sub platform corresponding to the production linedevice may also retrieve the target object data in the corresponding subplatform database and package the target object data to the generalplatform of the sensor network platform. Thus, the management platformcan know the device parameters in advance, give early warning, performcorresponding early warning operations, and issue control commands, soas to solve many problems caused by data delay.

Further, in the case that there are early warning parameter value andfixed parameter value at the same time, when the real-time parametervalue is greater than the early warning parameter value and fixedparameter value, the sub platform corresponding to the production linedevice may take the corresponding fixed parameter value as prioritydata, and take the fixed parameter value and real-time parameter valueas target object data to package them to the general platform of thesensor network platform. During the production process of the productionline device, a parameter may change rapidly in one time period, so itmay exceed the early warning parameter value and the fixed parametervalue at the same time or time period. At this time, the device is inurgent need of corresponding operations. Therefore, we take the fixedparameter value as the priority data, and give priority to packaging thefixed parameter value and the real-time parameter value as the targetobject data to the general platform of the sensor network platform.Thus, the corresponding platform or platform operators can determine thelatest status of the device and give the fastest treatment to protectthe device when obtaining the target object data.

In specific application, in order to facilitate processing,classification and interpretation of data, that the general platform ofthe sensor network platform may receive packaged data, generate compiledfiles sorted by the process sequences according to the correspondingproduction line device and the device data collectors, classify thecompiled files according to the process sequences, and store them in thesecond server specifically include the following: the general platformof the sensor network platform may store threshold data tables andreal-time data tables sorted according to the process sequences inadvance; the general platform of the sensor network platform may receivethe packaged data and compile it according to the process sequences, andcompile the threshold data and the real-time data of all the sameprocesses into the corresponding threshold data tables and real-timedata tables to form sequential compiled files; the second server may bearranged with a plurality of independent sub platform servers accordingto the process sequences, and the compiled files may be stored throughindependent sub platform servers of the corresponding processes,respectively. The threshold data tables and the real-time data tablesmay be sorted according to the process sequences, each of whichcorrespond to the data of the production line device and the device datacollectors in each process, so that the data can be independent and easyto be processed whether in data processing, transmission or datainteraction.

In specific applications, in order to further reduce the amount of dataanalysis, reduce number of data conversion and facilitate datainteraction, that the first server may receive or retrieve the compiledfiles in corresponding sequences, analyze the compiled files, and senddifferent control instructions to the corresponding second server basedon analysis results specifically include the following: the first servermay be preset with a real area map matching with installationenvironment of the production line devices, the production line devicesmay be named successively according to process sequences of assemblyline product manufacturing in the real area map, and each productionline device may be equipped with a parameter comparison table; theparameter comparison table may at least comprise a standard data columnand a comparison data column; after the first server receives orretrieves the compiled files in the corresponding sequence, the compiledfiles may be divided into sections successively according to the processsequences, and the target object data in the compiled filescorresponding to each section may be mapped to the parameter comparisontable of the production line device of the corresponding process,wherein the threshold data and the real-time data may correspond to thestandard data column and the comparison data column respectively; whenthe real-time data exceeds the threshold data, the first server mayassociate and identify the corresponding production line device in thereal area map; the first server may send the different controlinstructions to different production line device after identification,package all the different control instructions in sequence according tothe process sequences, and then transmit them to the correspondingsecond server.

Based on this, when the first server performs corresponding operation orcontrol, it can view and compare the corresponding device of thecorresponding process in the real area map, and view the correspondingdata in the parameter comparison table through mapped data, so as toconduct intuitive operation and identification. In addition, the realarea map and the parameter comparison table can be also directly used asdata source to interact with the user platform, which is also convenientfor the user platform to view and operate the corresponding data.

It should be noted that, the first server may associate and identify thecorresponding production line device in the real area map, which refersto that: when the real-time data of the production line device exceedsthe threshold data, the corresponding production line device may havedifferent identification effects from other normal production linedevices in the real area map. For example, the parameters of the normalproduction line device may be unified into colorless identification,while the production line device whose real-time data exceeds thethreshold data may be identified by other colors, such as red, orange,etc. In addition, color flicker may be used for further distinction.During identification, difference between the instant data and thethreshold data may also be identified according to different colors. Forexample, the difference between the instant data and the threshold datamay be distinguished from small to large identified by light yellow,yellow, orange, light red, red, dark red, etc. Therefore, the differencebetween the instant data and the threshold data may also be identifiedaccording to the color to reflect a size of corresponding data of theproduction line device, then hazard level of the production line devicemay be obtained in reverse.

Immediately above, in order to facilitate information exchange and speedup information feedback, each production line device in the real areamap may be also configured with a control instruction table, the controlinstruction table stores a plurality of the control instructions andcontrol instruction data packets associated with the controlinstructions, when the first server sends corresponding controlinstructions corresponding to different control instruction tables, thecontrol instruction data packets of different control instructions maybe packaged successively according to the process sequences andtransmitted to the corresponding second server.

Further, in the compiled files, data segmentation characters may be setbetween different processes, and the compiled files may be segmentedinto same number of node data as the number of the assembly line productmanufacturing process through the data segmentation characters. Thus,when packaging, summarizing and decomposing in different processes, thedata segmentation characters may be referred to operate, which may savedata processing speed compared with identifying huge data nodes.

FIG. 2 shows a flow chart of the control method of the industrialInternet of things with a sensor network platform in a front subplatform type according to some embodiments in the present disclosure.

As shown in FIG. 2 , the second embodiment of the present disclosureaims to provide a control method of industrial Internet of things with asensor network platform in a front sub platform type, the industrialInternet of things with the sensor network platform in the front subplatform type includes: a user platform, a service platform, amanagement platform, a sensor network platform, and an object platformwhich are interacted sequentially.

The user platform is configured as a terminal device interacting with auser. The service platform is configured as a first server andconfigured to: receive instructions of the user platform and send themto the management platform, and extract information required for theuser platform from the management platform, process the information, andsend the information to the user platform. The management platform isconfigured as a second server, and configured to control an operation ofthe object platform, and receive feedback data of the object platform.The sensor network platform is configured as a communication network anda gateway and configured to perform interaction between the objectplatform and the management platform. The object platform is configuredas production line devices and device data collectors for performmanufacture.

The service platform may adopt centralized layout, the managementplatform may adopt independent layout, and the sensor network platformmay adopt front sub platform layout. The centralized layout may meanthat the service platform uniformly receives data, uniformly processesdata and uniformly sends data. The independent layout may mean that themanagement platform adopts different sub platforms for data storage,data processing and/or data transmission for different types of data.The front sub platform layout may mean that the sensor network platformis provided with a general platform and a plurality of sub platforms,the plurality of sub platforms respectively store and process data ofdifferent types or data of different receiving objects sent by theobject platform, and the general platform stores and processes data ofthe plurality of sub platforms after summarizing the data of theplurality of sub platforms, and transmits the data to the managementplatform.

The control method comprises the following steps. The production linedevice and the device data collectors of the object platform may bedivided into a plurality of target objects in different processsequences according to processes of manufacturing an assembly lineproduct, and a device data collector of a same target object may be usedto collect real-time data of the production line device of acorresponding process. Each sub platform of the sensor network platformmay respectively correspond to target object data of different processsequences, the target object data may include threshold data of theproduction line device stored in each sub platform database and thereal-time data collected by the device data collector, the sub platformdata database may be configured in the gateway. When the real-time dataof the production line device is greater than the threshold data, thesub platform corresponding to the production line device may call thetarget object data in the corresponding sub platform database, packagethe data and transmit it to the general platform of the sensor networkplatform. The general platform of the sensor network platform mayreceive the packaged data, generate compiled files sorted by the processsequences according to the corresponding the production line device andthe device data collectors, classify the compiled files according to theprocess sequences and store them in the second server. The first servermay receive or retrieve the compiled files in corresponding sequencesand analyze the compiled files, and send different control instructionsto the corresponding second server based on analysis results. The firstserver may sort and classify the control instructions according to theprocess sequences of the object platform, and generate classifiedcontrol instructions corresponding to the process sequences, the secondserver may generate configuration files of different types correspondingto the process sequences according to the classified controlinstructions, and send the configuration files to the general platformof the sensor network platform for summary and storage. Each subplatform database of the sensor network platform may correspond to eachinformation channel, the general platform of the sensor network platformmay send the configuration files to the corresponding sub platforms,respectively, and the sub platforms may control the correspondingproduction line device and device data collectors to execute thecorresponding control instructions according to the configuration files,respectively.

FIG. 3 shows an exemplary flowchart for a process of determining abroadband allocation scheme according to some embodiments in the presentdisclosure. As shown in FIG. 3 , process 300 contains the followingsteps. In some embodiments, process 300 may be performed by the sensornetwork platform.

In some embodiments, each platform included in the industrial Internetof things with a sensor network platform in a front sub platform typemay be applicable to the scene of automobile engine assembly andproduction. For example, the object platform may be configured withmultiple production line devices and multiple device data collectors,where, the production line devices may include cylinder processingdevices, cylinder positioning and turnover devices, cam assemblyinstallation devices, bolt assembly installation devices, machine filterassembly, oiling devices, etc. The device data collectors may includevarious types of sensors (e.g., thermometers, barometers, etc.) and mayalso include image and/or video data acquisition devices (e.g., videomonitors, etc.). The sensor network platform may be configured as acommunication network and a gateway. Each sub platform of the sensornetwork platform may configure a gateway according to the target objectdata of different process sequences. The target object data may includea data volume threshold of each production line device and data uploadedby the device data collectors (such as the monitoring data).

In some embodiments, the processes of manufacturing an assembly lineproduct corresponding to the object platform may include variousprocesses of the automobile engine assembly and production. For example,the automobile production process may include 22 process flows, forexample, 1. the cylinder block is facing down, the cylinder block,crankshaft and camshaft are fed, cleaned, blown, and the diesel enginemodel and label are printed; 2. after the cylinder block is turned 180°,mark it for confirmation; 3. after the cylinder block is turned over,the ground of the cylinder block is upward, etc.

In some embodiments, the production line devices and the device datacollectors of the object platform may be divided into a plurality oftarget objects with different process sequences according to the processof assembly line of automobile production and assembly, and a devicedata collector of the same target object may collect the real-time dataof the production line device(s) of the corresponding process. Forexample, in a first process of the automobile production process, thevideo monitor of the corresponding process may obtain video data of anoperator during execution of the process.

In some embodiments, the control method of industrial Internet of thingswith a sensor network platform in a front sub platform type furtherincludes: predicting an amount of the monitoring data during theproduction of a production line device collected by a device datacollector corresponding to the process; and determining a broadbanddistribution scheme of the monitoring data transmitted by acorresponding sub platform of the sensor network platform based onpredicted amount of the monitoring data.

In step 310, predicting an amount of the monitoring data during theproduction of a production line device collected by a device datacollector corresponding to a process.

In some embodiments, the target object data for a process may includemonitoring data for that process. The monitoring data may refer to imageand/or video data about the process obtained by a video monitor (such asa camera). For example, in the first process of an automobile productionand assembly line, working images of the process may be collected by oneor more cameras corresponding to the first process.

The amount of the monitoring data may refer to an amount of datacontained in the monitoring data. In some embodiments, the longer thevideo of the monitoring data, the larger the amount of the correspondingmonitoring data.

In some embodiments, based on relevant contents of the process, theamount of monitoring data of the corresponding process may be predicted.

In some embodiments, the amount of the monitoring data may be predictedbased on proficiency of an operator of the production line devicecorresponding to the process, relevant parameters of the process andshooting parameters of the process.

The operator proficiency (or proficiency of the operator) refers toproficiency of production personnel of the corresponding process inoperations of the production process. In some embodiments, the operatorproficiency may be determined by working time of the productionpersonnel in the corresponding production process. For example, thelonger an operator works, the higher his proficiency may be considered.

The relevant parameters of the process may refer to parameters relatedto production characteristics of the process. In some embodiments, therelevant parameters of the process may include production type of theprocess and recheck proportion of the production process.

The production type of the process refers to an operation type completedby the operators when executing the corresponding process. For example,the production type of the process may include any combination of one ormore of installation type, inspection record type, measurement type,etc.

The recheck proportion of the production process refers to recheck ofsome parameters of the products of the completed process after theproduction process of the process is completed. In some embodiments, apart whose parameters are to be rechecked may be a part with highquality requirements in the process. For example, after process ofhoisting a cylinder head is completed, it may be necessary to recheckparameters such as a clearance width between the cylinder head and thecylinder body, an overall tightness. In some embodiments, the recheckproportion may be set by administrators.

The shooting parameters of the process refer to relevant parameterscaptured by video monitors corresponding to the process. In someembodiments, the shooting parameters of the process may include shootingangles of the corresponding video monitors and fineness of the videoimages (pictures). The shooting angles may ensure that lens can obtainpictures of the whole operation flow of the corresponding process. Thefineness of the video pictures may be determined by adjusting focallengths and picture definition of the video monitors.

In some embodiments, the amount of the monitoring data may be calculatedand predicted by the weight proportion of the above parameters based onthe operator proficiency of the production line device corresponding tothe process, the relevant parameters of the process and the shootingparameters of the process.

In some embodiments, the amount of the monitoring data may be predictedby a data amount prediction model, the data amount prediction model maybe a multi classification model. In some embodiments, the data amountprediction model may be a machine learning model, including any one or acombination of a deep neural network model, a recurrent neural networkmodel, a convolutional neural network, or other customized modelstructures.

In some embodiments, the input of the data amount prediction model mayinclude the operator proficiency of the production line devicecorresponding to the process, the relevant parameters of the process andthe shooting parameters of the process. The output of the data amountprediction model may be a data group containing a plurality ofprobability values, such as a probability vector composed of a pluralityof probability values. The probability vector can reflect a probabilitythat a predicted amount of monitoring data belongs to a range of anamount of monitoring data corresponding to each tag. A label cancorrespond to a range of an amount of the monitoring data. The value ofthe range of the monitoring data and the starting range may be setfreely. For example, tag 1 may indicate 100-90 Mb, tag 2 may indicate90-80 Mb, tag 3 may indicate 80-70 Mb, etc. The output of the dataamount prediction model may include the probability values correspondingto each tag. For example, the output of the data volume prediction modelmay be (0.1, 0.2, 0.7), where 0.1 means that the probability that thepredicted value belongs to the range corresponding to label 1 is 10%,0.2 means that the probability that the predicted value belongs to therange corresponding to label 2 is 20%, and 0.7 means that theprobability that the predicted value belongs to the range correspondingto label 3 is 70%.

In some embodiments, the data amount prediction model may be acquiredbased on training. The training of the data amount prediction model maybe performed by the general platform of the sensor network.

In some embodiments, when training the data amount prediction model, aplurality of labeled training samples may be used for training through avariety of methods (e.g., gradient descent method), so that parametersof the data amount prediction model may be learned. When the traineddata amount prediction model meets preset conditions, the training ends,and the trained data amount prediction model is obtained.

The training samples may include the operator proficiency of theproduction line devices corresponding to historical processes, relevantparameters of the historical processes and shooting parameters of thehistorical processes. Labels of the training samples may be the amountof historical monitoring data, such as 98, 85, 72, etc., and the labelsof the training samples may be obtained through manual labeling. In someembodiments, the data amount prediction model may be trained in anotherdevice or module.

In step 320, determining a broadband distribution scheme of themonitoring data transmitted by a corresponding sub platform of thesensor network platform based on the predicted amount of the monitoringdata.

The broadband distribution scheme refers to the situation that differentbroadband sizes are allocated to sub platforms of the sensor networkplatform. A sensor network sub platform may correspond to a process withcomplex operation types or long operation time, and a large bandwidthneeds to be allocated to the sensor network sub platform. For example,generally speaking, an installation process is more complex and takeslonger than an inspection record process, and the broadband allocated tothe sensor network sub platform corresponding to the installationprocess may be larger.

In some embodiments, the broadband distribution scheme for themonitoring data transmitted by a corresponding sub platform of thesensor network platform may be determined by setting complexity and timelength of the process operation. For example, the complexity of theprocess operation may be rated from 1 to 10. The higher the complexity,the greater the number, and the larger the corresponding distributedbroadband. For example, the broadband distributed to (by) the subplatform of the sensor network platform for the process with complexityof 5 may be 20 MHz; the broadband distributed to the sensor network subplatform corresponding to the process with a complexity of 8 may be 32MHz. For another example, broadband traffic may be distributed based onthe time length of the process operation. For example, the broadbanddistributed to the process corresponding to a time length of 30 minutemay be 15 MHz; the broadband distributed to the process corresponding toa time length of one hour may be 30 MHz.

In some embodiments of the present disclosure, the amount of monitoringdata may be predicted through the machine learning model, and based onthe predicted amount of the monitoring data, the broadband distributionscheme for the monitoring data of the corresponding sub platform of thesensor network platform may be determined, so that the data situationcan be predicted in advance to prevent the slow transmission speed anduntimely data acquisition caused by the large amount of data.

It should be noted that the above description of process 300 is only forexample and explanation, and does not limit the scope of application ofthe present disclosure. For those skilled in the art, variousmodifications and changes can be made to process 300 under the guidanceof the present disclosure. However, these amendments and changes arestill within the scope of the present disclosure.

FIG. 4 shows an exemplary flowchart for preprocessing monitoring dataaccording to some embodiments in the present disclosure. As shown inFIG. 4 , process 400 includes the following steps. In some embodiments,process 400 may be performed by the general platform of the sensornetwork.

In step 410, determining whether the amount of the monitoring data isgreater than a first threshold.

The first threshold may be a maximum amount of monitoring data which istransmissible with broadband distributed by the sub platform of thesensor network platform of the corresponding process.

In some embodiments, each broadband distributed by each sub platform ofthe sensor network platform is different, and each first thresholdcorresponding to each sub platform may be different. In someembodiments, the first threshold may be freely set by administrators.

In some embodiments, the amount of monitoring data transmissible withthe broadband of the sub platform of the sensor network platform may bethe maximum amount of the monitoring data recorded under the conditionthat operators of the corresponding process operate normally. When theoperators make mistakes, the whole operation flow may become longer, andthe amount of the monitoring data recorded may also become larger, thusthe amount of the monitoring data may exceed the first threshold.

In step 420, preprocessing the monitoring data before transmitting themonitoring data in response to the amount of the monitoring data beinggreater than the first threshold.

In some embodiments, in response to the amount of the monitoring databeing greater than the first threshold, the general platform of thesensor network may preprocess the monitoring data before transmittingthe monitoring data. Preprocess refers to processing the correspondingmonitoring data to reduce the amount of the monitoring data. Thepreprocess steps are as follows:

In step 421, determining video key frames of the monitoring data.

Video key frames refer to frames in a monitoring video that includes keyactions of the production line device operation or the operators in theoperation process. Among them, key actions may be first actions foroperators to start the operation, or the most important actions in theoperation process. For example, a key action may include an alignment ofthe cylinder head and the cylinder body by the production device,placement of the cylinder head, and other important actions.

In some embodiments, each frame image of the video of the monitoringdata may be scored based on a key frame scoring model; images of thevideo whose scores are greater than a threshold may be determined as thevideo key frames.

In some embodiments, the key frame scoring model may be a machinelearning model, including any one or combination of a deep neuralnetwork model, a recurrent neural network model, a convolutional neuralnetwork, or other customized model structures.

In some embodiments, the key frame scoring model may include a featureextraction layer and a scoring layer. The feature extraction layer canbe used to extract features of key actions in the images, and thescoring layer can score each frame of the images based on features ofthe images. In some embodiments, an output of the feature extractionlayer may be used as an input of the scoring layer. An input of thefeature extraction layer may be monitoring video images, and the outputof the feature extraction layer may be a feature vector of each videoimage frame. The input of the scoring layer may be the feature vector ofeach video image frame, and the output of the scoring layer may be ascore of each video image frame.

In some embodiments, the key frame scoring model may be acquired basedon training. Training of the key frame scoring model may be performed bythe general platform of the sensor network.

In some embodiments, when the key frame scoring model is trained, aplurality of labeled training samples may be used, wherein the featureextraction layer and the scoring layer may be jointly trained.Specifically, the labeled training samples may be input into an initialfeature extraction layer, parameters of the initial feature extractionlayer and an initial scoring layer are updated through training until atrained intermediate feature extraction layer and a trained intermediatescoring layer meet the preset conditions to obtain the trained featureextraction layer and the trained scoring layer so as to learn parametersof the key frame scoring model. When the key frame scoring modeltraining meets preset conditions, the training may end, the trained keyframe scoring model may be obtained.

In some embodiments, in the training of the key frame scoring model, ascore of a start frame with a key operation may be marked as 1, andimage similarities between other frames and the start frame may be usedas labels of other frames. The similarities may be denoted as any valuebetween [0,1], the larger the values, the higher the similarities. Insome embodiments, the similarities may be determined based on analgorithm. For example, images of the key frames may be converted into avector representation, the Euclidean distances or Manhattan distancesbetween other frames and the key frame may be calculated, and thesimilarities may be further calculated according to the distanceresults.

In step 422, extracting video images based on a specific range beforeand after the video key frames.

Generally speaking, the monitoring video may include the whole operationprocedure of the corresponding process, including empty lenses excludingoperator operations and the production line device operation. In someembodiments, the video images may be extracted based on the specificrange before and after the video key frames, and the extracted videoimages may only include all actions of the production line deviceoperation or the operator operations.

Some embodiments of the present disclosure extract video images in thespecific range through the key frames of the video (or video keyframes), which can reduce unnecessary content in the monitoring data,reduce the transportation pressure of the broadband, and facilitatemanagers to quickly obtain useful monitoring data content.

In step 423, determining whether the amount of the monitoring data isgreater than second threshold.

The second threshold may be a value that exceeds the maximum amount ofmonitoring data transmissible with the broadband distributed by the subplatform of the sensor network platform. In some embodiments, the secondthreshold may be greater than the first threshold.

In step 424, in response to the amount of the monitoring data greaterthan the second threshold, based on a node extraction model, extractingjoint nodes of the operator (or the operator's action, the operation inaction) from non key frame images before and after the key frames toreplace image data to be uploaded.

In some embodiments, when the amount of monitoring data is greater thanthe second threshold, based on a node extraction model, extracting jointnodes of the operator from non key frame images before and after the keyframes to replace image data and uploading.

In some embodiments, the non key frame images before and after the keyframes may be video images including non key frame portion of theextracted video images determined based on step 422.

In some embodiments, the node extraction model may be a machine learningmodel, including any one or combination of a deep neural network model,a recurrent neural network model, a convolutional neural network, orother customized model structures.

In some embodiments, inputs of the node extraction model may include nonkey frame images, and outputs of the node extraction model may be jointnodes of the operators in the images.

In some embodiments, the node extraction model may be acquired based ontraining. The training of the node extraction model may be performed bythe general platform of the sensor network.

In some embodiments, when training the node extraction model, aplurality of labeled training samples may be used for training through avariety of methods (e. g., gradient descent method), so that theparameters of the model may be learned. When the training model meetsthe preset conditions, the training may end and the trained nodeextraction model may be obtained.

The training samples may include historical monitoring videos, andlabels of the training samples may be joint nodes of the operators inthe historical monitoring videos, such as elbow joint, wrist joint,cervical vertebra, etc. The labels of training samples may be obtainedby manual annotation. In some embodiments, the node extraction model maybe trained in another device or module.

In some embodiments, the general platform of the sensor network may alsoadjust a preset sampling rate in real time based on similarities betweenthe key frame and other frame images adjacent to the key frame. Thesampling rate may be a ratio of a number of uploaded frames to a totalnumber of monitoring video frames.

In some embodiments, the similarities may be determined by convertingeach frame image in the video into a vector representation andcalculating distances between the key frame and other adjacent framesthereto. In some embodiments, the higher a similarity, the smaller theadjustment of the corresponding sampling rate.

In some embodiments, the adjustment of the sampling rate may bedetermined by a functional relationship between the sampling rate andthe similarity. As shown in formula (1):

α=g(1−f)+h  (1)

where α denotes the preset sampling rate, f denotes the similarity, gand h denotes constants.

For more information on the sample rate adjustment, see FIG. 5 and itsdetailed description.

Some embodiments of the present disclosure can reduce the amount of dataof the monitoring video, reduce the uploading of useless information,and reduce the pressure of broadband for data transmission by replacingthe image data with joint node information of the operators.

FIG. 5 shows an exemplary flowchart for determining a sampling rate ofmonitoring data according to some embodiments in the present disclosure.

In some embodiments, the sampling rate 520 of the monitoring data may bedetermined based on the predicted amount of the monitoring data 510.

In some embodiments, the predicted amount of the monitoring data 510 maybe negatively correlated with the sampling rate 520, and the larger thepredicted amount of the monitoring data, the lower the sampling rate. Insome embodiments, an adjustment multiple of the sampling rate 520 may bedetermined based on a mathematical relationship between the samplingrate 520 and the amount of the monitoring data 510.

In some embodiments, the adjustment multiple of the sampling rate 520may be determined by setting a threshold value of the amount of themonitoring data 510. For example, the threshold of the amount of themonitoring data may include a first threshold, a second threshold, athird threshold . . . . When the predicted amount of the monitoring dataexceeds the first threshold and is less than the second threshold, thesampling rate may be adjusted to 0.75 times the preset sampling rate;when the predicted amount of the monitoring data exceeds the secondthreshold and is less than the third threshold, the sampling rate may beadjusted to 0.5 times the preset sampling rate, and so on.

In some embodiments, the adjustment multiple of the sampling rate mayalso be determined based on functional relationship between the samplingrate 520 and the amount of the monitoring data 510. As shown in formula(2):

j=[c/(p+d)]+e  (2),

where, j represents the adjustment multiple of the sampling rate, p isthe amount of monitoring data, and c, d, e denote constants.

In some embodiments, an adjustment amplitude of the sampling rate 520may be determined based on confidence degrees of output results of thedata amount prediction model.

In some embodiments, the confidence degrees may be probability valuesthat the predicted amount of monitoring data output based on the dataamount prediction model falls within a range interval of each amount ofmonitoring data. For example, based on the data amount prediction modeldescribed in step 310, the output of the data amount prediction modelmay be (0.1, 0.2, 0.7), where each probability value corresponding toeach interval may be expressed as the confidence degree corresponding tothe range interval of the amount of monitoring data. In someembodiments, the adjustment amplitude (or adjustment range) of thesampling rate may be determined based on a greatest range interval ofthe confidence degrees. For example, the maximum confidence degree (orcoefficient) may be 0.7, and the corresponding interval of the amount ofthe monitoring data may be 80-70 Mb. In some embodiments, the smallerthe range interval of the amount of monitoring data corresponding to theconfidence coefficient, the smaller the adjustment amplitude of thesampling rate.

In some embodiments, the adjustment amplitude of the sampling rate maybe determined by setting a confidence degree threshold. For example, theconfidence degree threshold may include the first confidence threshold,the second confidence threshold, the third confidence threshold, etc.When the confidence degree is higher than the first confidence degreethreshold, the adjustment amplitude of the sampling rate is 0.8 times;when the confidence degree is higher than the second confidence degreethreshold and less than the first confidence degree threshold, theadjustment range of the sampling rate is 0.9 times; when the confidenceis less than the second confidence threshold, the sampling rate is notadjusted.

In some embodiments, the adjustment amplitude of the sampling rate mayalso be determined by a functional relationship between the confidencedegree and the sampling rate. As shown in formula (3):

r=ks+e  (3),

where, r denotes the adjustment amplitude of the sampling rate, and thevalue of r denotes in the range of the interval of (0,1]; s denotes theconfidence degree; k and e denotes constants.

Some embodiments of the present disclosure control the amount of themonitoring data by adjusting the sampling rate, which can ensure thatwhile obtaining effective data content, the amount of data can bereduced, the broadband transmission pressure can be reduced, theoperation speed of the transmission network platform can be improved,and the data management of the platform can be facilitated.

The basic concepts have been described above. Obviously, for thoseskilled in the art, the above detailed disclosure is only an example anddoes not constitute a limitation of the present disclosure. Although itis not explicitly stated here, those skilled in the art may make variousmodifications, improvements and amendments to the present disclosure.Such modifications, improvements and amendments are suggested in thepresent disclosure, so such modifications, improvements and amendmentsstill belong to the spirit and scope of the exemplary embodiments of thepresent disclosure.

Meanwhile, the present disclosure uses specific words to describe theembodiments of the present disclosure. For example, “one embodiment”,and/or “some embodiments” mean a certain feature or structure related toat least one embodiment of the present disclosure. Therefore, it shouldbe emphasized and noted that “one embodiment” or “an alternativeembodiment” mentioned twice or more in different positions in thepresent disclosure does not necessarily refer to the same embodiment. Inaddition, certain features or structures in one or more embodiments ofthe present disclosure may be appropriately combined.

In addition, unless explicitly stated in the claims, the sequence ofprocessing elements and sequences, the use of numbers and letters, orthe use of other names described in the present disclosure are not usedto define the sequence of processes and methods in the presentdisclosure. Although the above disclosure has discussed some currentlyconsidered useful embodiments of the invention through various examples,it should be understood that such details are only for the purpose ofexplanation, and the additional claims are not limited to the disclosedembodiments. On the contrary, the claims are intended to cover allamendments and equivalent combinations that conform to the essence andscope of the embodiments of the present disclosure. For example,although the system components described above can be implemented byhardware devices, they can also be implemented only by softwaresolutions, such as installing the described system on an existing serveror mobile device.

Similarly, it should be noted that, in order to simplify the descriptiondisclosed in the present disclosure and thus help the understanding ofone or more embodiments of the invention, the foregoing description ofthe embodiments of the present disclosure sometimes incorporates avariety of features into one embodiment, the drawings or the descriptionthereof. However, this disclosure method does not mean that the objectof the present disclosure requires more features than those mentioned inthe claims. In fact, the features of the embodiments are less than allthe features of the single embodiments disclosed above.

In some embodiments, numbers describing the number of components andattributes are used. It should be understood that such numbers used inthe description of embodiments are modified by the modifier “about”,“approximate” or “generally” in some examples. Unless otherwise stated,“approximately” or “generally” indicate that a ±20% change in the figureis allowed. Accordingly, in some embodiments, the numerical parametersused in the description and claims are approximate values, and theapproximate values can be changed according to the characteristicsrequired by individual embodiments. In some embodiments, the numericalparameter should consider the specified significant digits and adopt themethod of general digit reservation. Although the numerical fields andparameters used to confirm the range breadth in some embodiments of thepresent disclosure are approximate values, in specific embodiments, thesetting of such values is as accurate as possible within the feasiblerange.

For each patent, patent application, patent application disclosure andother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, etc., the entirecontents are hereby incorporated into the present disclosure forreference. Except for the application history documents that areinconsistent with or conflict with the contents of the presentdisclosure, and the documents that limit the widest range of claims inthe present disclosure (currently or later appended to the presentdisclosure). It should be noted that in case of any inconsistency orconflict between the description, definitions, and/or use of terms inthe supplementary materials of the present disclosure and the contentsdescribed in the present disclosure, the description, definitions,and/or use of terms in the present disclosure shall prevail.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other deformations may also fallwithin the scope of the present disclosure. Therefore, as an examplerather than a limitation, the alternative configuration of theembodiments of the present disclosure can be regarded as consistent withthe teachings of the present disclosure. Accordingly, the embodiments ofthe present disclosure are not limited to those explicitly introducedand described in the present disclosure.

What is claimed is:
 1. Industrial Internet of things with a sensornetwork platform in a front sub platform type, comprising: a userplatform, a service platform, a management platform, a sensor networkplatform, and an object platform which are interacted sequentially,wherein the user platform is configured as a terminal device interactingwith a user; the service platform is configured as a first server andconfigured to: receive an instruction of the user platform and send theinstruction to the management platform, and extract information requiredfor the user platform from the management platform, process theinformation, and send the information to the user platform; themanagement platform is configured as a second server and configured tocontrol an operation of the object platform, and receive feedback dataof the object platform; the sensor network platform is configured as acommunication network and a gateway and configured for interactionbetween the object platform and the management platform; the objectplatform is configured as production line devices and device datacollectors for manufacture; wherein the service platform adoptscentralized layout, the management platform adopts independent layout,and the sensor network platform adopts front sub platform layout;wherein the centralized layout means that the service platform uniformlyreceives data, uniformly processes data and uniformly sends data; theindependent layout means that the management platform adopts differentsub platforms for data storage, data processing and/or data transmissionfor different types of data; the front sub platform layout means thatthe sensor network platform is provided with a general platform and aplurality of sub platforms, the plurality of sub platforms respectivelystore and process data of different types or data of different receivingobjects sent by the object platform, and the general platform stores andprocesses data of the plurality of sub platforms after summarizing thedata of the plurality of sub platforms, and transmits the data to themanagement platform; the production line devices and the device datacollectors of the object platform are divided into a plurality of targetobjects in different process sequences according to processes ofmanufacturing an assembly line product, and a device data collector of asame target object is used to collect real-time data of a productionline device of a corresponding process; each sub platform of the sensornetwork platform corresponds to target object data of each of differentprocess sequences, the target object data includes threshold data of aproduction line device stored in each sub platform database andreal-time data collected by a device data collector, the sub platformdatabase is configured in the gateway; when the real-time data of theproduction line device is greater than the threshold data, the subplatform corresponding to the production line device retrieves thetarget object data in the corresponding sub platform database, packagesthe data and transmits it to the general platform of the sensor networkplatform; the general platform of the sensor network platform receivesthe packaged data, generates compiled files sorted according to theprocess sequences based on the corresponding production line device andthe device data collector, classifies the compiled files according tothe process sequences, and stores them in the second server; the firstserver receives or retrieves the compiled files in the correspondingsequences, analyzes the compiled files, and sends different controlinstructions to the corresponding second server based on analysisresults; the first server sorts and classifies the control instructionsaccording to the process sequences of the object platform, and generatesthe classified control instructions corresponding to the processsequences, the second server generates configuration files of differenttypes corresponding to the process sequences according to the classifiedcontrol instructions, and sends the configuration files to the generalplatform of the sensor network platform for summary and storage; andeach sub platform database of the sensor network platform corresponds toeach information channel, the general platform of the sensor networkplatform sends the configuration files to the corresponding subplatforms, respectively, and the sub platforms control the correspondingproduction line devices and device data collectors to execute thecorresponding control instructions according to the configuration files,respectively.
 2. The industrial Internet of things with the sensornetwork platform in the front sub platform type of claim 1, wherein whenthere are one or more sub processes in a same process, the productionline devices and device data collectors corresponding to the one or moresub processes are divided into multiple sub target objects of differentsub process sequences according to a sequence of manufacturing theassembly line product, sub target object data of each sub target objectincludes threshold data and real-time data corresponding to the each subtarget object, all sub target object data are sorted according to thesequence of manufacturing the assembly line product, packaged andsummarized, and used as target object data of the same process.
 3. Theindustrial Internet of things with the sensor network platform in thefront sub platform type of claim 1, wherein the threshold data of theproduction line device includes a fixed parameter value of a maximumthreshold allowed by the corresponding production line device duringproduct manufacturing, the real-time data is a real-time parameter valuecollected by the device data collector of the corresponding productionline device according to a predetermined time, and the real-timeparameter value and the fixed parameter value belong to a same parametertype.
 4. The industrial Internet of things with the sensor networkplatform in the front sub platform type of claim 3, wherein thethreshold data of the production line device includes an early warningparameter value corresponding to an early warning threshold set by theproduction line device during the product manufacturing, and the earlywarning parameter value is 70%˜90% of the fixed parameter value; andwhen the real-time parameter value of the device data collector isgreater than the early warning parameter value, the sub platformcorresponding to the production line device also retrieves the targetobject data in the corresponding sub platform database and packages thedata to the general platform of the sensor network platform; and at thistime, the target object data includes the early warning parameter valueand the real-time parameter value.
 5. The industrial Internet of thingswith the sensor network platform in the front sub platform type of claim4, wherein when the real-time parameter value is greater than the earlywarning parameter value and the fixed parameter value, the sub platformcorresponding to the production line device takes the correspondingfixed parameter value as priority data, and package s the fixedparameter value and the real-time parameter value as the target objectdata in priority to the general platform of the sensor network platform.6. The industrial Internet of things with the sensor network platform inthe front sub platform type of claim 1, wherein that the generalplatform of the sensor network platform receives the packaged data,generates compiled files sorted according to the process sequences basedon the corresponding production line device and the device datacollector, classifies the compiled files according to the processsequences, and stores them in the second server includes: storingthreshold data tables and real-time data tables sorted according to theprocess sequences in advance; receiving the packaged data and compilesit according to the process sequences, and compiles the threshold dataand the real-time data of the same process into the correspondingthreshold data tables and real-time data tables to form the compiledfiles in sequence; wherein the second server is arranged with aplurality of independent sub platform servers according to the processsequences, and the compiled files are stored through independent subplatform servers of the corresponding processes, respectively.
 7. Theindustrial Internet of things with the sensor network platform in thefront sub platform type of claim 1, wherein to receive or retrieve thecompiled files in corresponding sequences and analyze the compiledfiles, and send different control instructions to the correspondingsecond server based on analysis results, the first server is preset witha real area map matching with installation environment of the productionline devices, the production line devices in the real area map are namedsuccessively according to process sequences of manufacturing theassembly line product, and each production line device has a parametercomparison table, wherein the parameter comparison table at leastcomprises a standard data column and a comparison data column; after thefirst server receives or retrieves the compiled files in thecorresponding sequences, the compiled files are divided into sectionssuccessively according to the process sequences, and the target objectdata in the compiled files corresponding to each section is mapped tothe parameter comparison table of the production line device of thecorresponding process, wherein the threshold data and the real-time datacorrespond to the standard data column and the comparison data column,respectively; when the real-time data exceeds the threshold data, thefirst server associates and identifies the corresponding production linedevice in the real area map; the first server sends the differentcontrol instructions to different identified production line devices,packages all the different control instructions successively accordingto the process sequences, and then transmits them to the correspondingsecond server.
 8. The industrial Internet of things with the sensornetwork platform in the front sub platform type of claim 7, wherein eachproduction line device in the real area map is also configured with acontrol instruction table, the control instruction table stores aplurality of control instructions and control instruction data packetsassociated with the control instructions, and when the first serversends control instructions corresponding to the different controlinstruction tables, control instruction data packets of the differentcontrol instructions are packaged successively according to the processsequences and transmitted to the corresponding second server.
 9. Theindustrial Internet of things with the sensor network platform in thefront sub platform type of claim 8, wherein in the compiled files, datasegmentation characters are set between different processes, and thecompiled files are segmented into node data through the datasegmentation characters, the number of the node data is the same as thenumber of processes of manufacturing the assembly line product.
 10. Acontrol method of industrial Internet of things with a sensor networkplatform in a front sub platform type, wherein production line devicesand device data collectors of the object platform are divided into aplurality of target objects in different process sequences according toprocesses of manufacturing an assembly line product, and a device datacollector of a same target object is used to collect real-time data of aproduction line device of a corresponding process; each sub platform ofthe sensor network platform corresponds to target object data of each ofdifferent process sequences, the target object data includes thresholddata of a production line device stored in each sub platform databaseand real-time data collected by a device data collector, the subplatform database is configured in the gateway; and the control methodcomprising: when the real-time data of the production line device isgreater than the threshold data, by the sub platform corresponding tothe production line device, retrieving the target object data in thecorresponding sub platform database, packaging the data and transmittingit to the general platform of the sensor network platform; by thegeneral platform of the sensor network platform, receiving the packageddata, generating compiled files sorted according to the processsequences based on the corresponding production line device and thedevice data collector, classifying the compiled files according to theprocess sequences, and storing them in the second server; by the firstserver, receives or retrieves the compiled files in the correspondingsequences, analyzes the compiled files, and sends different controlinstructions to the corresponding second server based on analysisresults; by the first server, sorting and classifying the controlinstructions according to the process sequences of the object platform,and generating the classified control instructions corresponding to theprocess sequences, by the second server, generating different types ofconfiguration files according to the classified control instructionsaccording to the process sequences, and sending the configuration filesto the general platform of the sensor network platform for summary andstorage; and sending, by the general platform of the sensor networkplatform, the configuration files to the corresponding sub platforms,respectively, and controlling, by the sub platforms, the correspondingproduction line devices and device data collectors to execute thecorresponding control instructions according to the configuration files,respectively, wherein each sub platform database of the sensor networkplatform corresponds to each information channel.
 11. The control methodof claim 10, wherein target object data of a process includes monitoringdata of the process, and the control method further comprises:predicting an amount of the monitoring data during the production of aproduction line device collected by a device data collectorcorresponding to the process, wherein the amount of the monitoring datarefers to an amount of data contained in the monitoring data; anddetermining a broadband distribution scheme of the monitoring datatransmitted by a sub platform of the sensor network platform based onthe predicted amount of the monitoring data.
 12. The control method ofclaim 11, wherein the predicting an amount of the monitoring data duringthe production of a production line device collected by a device datacollector corresponding to the process comprises: predicting the amountof the monitoring data based on proficiency of an operator of theproduction line device corresponding to the process, relevant parametersof the process and shooting parameters of the process.
 13. The controlmethod of claim 11, further comprising: determining whether the amountof the monitoring data is greater than a first threshold; andpreprocessing the monitoring data before transmitting the monitoringdata in response to the amount of the monitoring data being greater thanthe first threshold.
 14. The control method of claim 11, furthercomprising: determining a sampling rate of the monitoring data based onthe predicted amount of the monitoring data.